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Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an RivestShamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RCDPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework. 

Resistant starch is a prebiotic accessed by gut bacteria with specialized amylases and starch-binding proteins. The human gut symbiont Ruminococcus bromii expresses Sas6 (Starch Adherence System member 6), which consists of two starch-specific carbohydrate-binding modules from family 26 (RbCBM26) and family 74 (RbCBM74). Here, we present the crystal structures of Sas6 and of RbCBM74 bound with a double helical dimer of maltodecaose. The RbCBM74 starch-binding groove complements the double helical α-glucan geometry of amylopectin, suggesting that this module selects this feature in starch granules. Isothermal titration calorimetry and native mass spectrometry demonstrate that RbCBM74 recognizes longer single and double helical α-glucans, while RbCBM26 binds short maltooligosaccharides. Bioinformatic analysis supports the conservation of the amylopectin-targeting platform in CBM74s from resistant-starch degrading bacteria. Our results suggest that RbCBM74 and RbCBM26 within Sas6 recognize discrete aspects of the starch granule, providing molecular insight into how this structure is accommodated by gut bacteria. 

Online social networks provide a convenient platform for the spread of rumors, which could lead to serious aftermaths such as economic losses and public panic. The classical rumor blocking problem aims to launch a set of nodes as a positive cascade to compete with misinformation in order to limit the spread of rumors. However, most of the related researches were based on a one-dimensional diffusion model. In reality, there is more than one feature associated with an object. A user’s impression on this object is determined not just by one feature but by her overall evaluation of all features associated with it. Thus, the influence spread of this object can be decomposed into the spread of multiple features. Based on that, we design a multi-feature diffusion model (MF-model) in this paper and formulate a multi-feature rumor blocking (MFRB) problem on a multi-layer network structure according to this model. To solve the MFRB problem, we design a creative sampling method called Multi-Sampling, which can be applied to this multi-layer network structure. Then, we propose a Revised-IMM algorithm and obtain a satisfactory approximate solution to MFRB. Finally, we evaluate our proposed algorithm by conducting experiments on real datasets, which shows the effectiveness of our Revised- IMM and its advantage to their baseline algorithms.